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Background: Advanced epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma had a high overall incidence of brain metastasis during the full course, and local brain radiotherapy combined with systemic targeted therapy may be a better strategy. This study aimed to identify the prognostic factors of EGFR-mutant brain-metastatic lung adenocarcinoma patients who received EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in combination with gamma knife radiosurgery.
Methods: Retrospective analysis of EGFR-mutant lung adenocarcinoma patients with brain metastases which developed at initial diagnosis or during EGFR-TKIs treatment period were performed. Intracranial progression free survival (PFS) was statistically analyzed between different subgroups to find out the prognostic factors including gender, age, smoking history, extracranial metastasis, EGFR mutation type, size and number of intracranial lesions, carcino-embryonic antigen (CEA) level, lung-molGPA score and so on.
Results: A total of 74 EGFR-mutant brain-metastatic lung adenocarcinoma patients were enrolled in this study, with median intracranial PFS of 14.7 months. One-year intracranial-progression-free rate was 58.5%, and two-year rate was 22.2%. Univariate survival analysis showed that patients with lower CEA level at initial diagnosis (<10 ng/L)(16.9 months vs 12.6 months, P=0.012) and smaller intracranial lesions (<2 cm)(15.4 months vs 10.8 months, P=0.021) and higher lung-molGPA score (>3)(15 months vs 12.6 months, P=0.041) were prone to have a superior intracranial PFS. Multivariate analysis showed that CEA≥10 ng/mL and intracranial lesion≥2 cm were the independent risk factors of intracranial PFS.
Conclusions: EGFR-TKIs in combination with gamma knife radiosurgery was an efficient treatment option to control the cranial tumor lesion. CEA≥10 μg/L at initial diagnosis and intracranial lesion≥2 cm were the risk factors of EGFR-mutant brain-metastatic lung adenocarcinoma patients receiving EGFR-TKIs in combination with gamma knife radiosurgery.
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http://dx.doi.org/10.3779/j.issn.1009-3419.2019.05.08 | DOI Listing |
Cisplatin resistance significantly limits the efficacy of chemotherapy in non-small cell lung cancer, necessitating the development of new strategies to overcome this barrier. This in vitro study aimed to elucidate the mechanism by which β-Ele reverses cisplatin resistance in lung adenocarcinoma cells via the LINC00511-mediated glycolysis and Wnt/β-catenin signaling pathways. The cisplatin-resistant human lung adenocarcinoma cell line (A549/DDP), with either LINC00511 overexpression or knockdown, was established through plasmid transfection.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Purpose: Bronchiolar adenoma (BA) is a rare benign pulmonary neoplasm originating from the bronchial mucosal epithelium and mimics lung adenocarcinoma (LAC) both radiographically and microscopically. This study aimed to develop a nomogram for distinguishing BA from LAC by integrating clinical characteristics and artificial intelligence (AI)-derived histogram parameters across two medical centers.
Methods: This retrospective study included 215 patients with diagnoses confirmed by postoperative pathology from two medical centers.
J Thorac Oncol
August 2025
Department of Radiation Medicine, Markey Cancer Center, University of Kentucky, Lexington, Kentucky.
Introduction: Cigarette smoking negatively affects lung cancer prognosis. Incorporating smoking history into stage-stratified survival analyses may improve prognostication.
Methods: Using the International Association for the Study of Lung Cancer ninth edition NSCLC database, we evaluated the association between smoking status at diagnosis and overall survival (OS) using Kaplan-Meier plots and multivariate Cox proportional hazard regression models adjusted for age, region, sex, histologic type, performance status, and TNM stage.
Brief Bioinform
August 2025
Department of Respiratory Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an 710004, China.
Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets.
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